Multi-depot and multi-model vehicle routing control method

Abstract

The multi-depot and multi-model vehicle route scheduling control method includes: Step 1, establishing an objective function with the goal of minimizing the total cost of all delivery vehicles; Step 2, encoding Step 3, population initialization; Step 4, using the objective function as the fitness function pair Evaluate all individuals; step 5, selection and crossover operation step 6, mutation operation; step 7, use the improved extreme value optimization algorithm to perform neighborhood search on each individual in the population; step 8, calculate the fitness of all individuals in the population ; Step 9, selection; Step 10, elite retention; Step 11, iteratively complete; Step 12, determine whether the termination condition is met, the termination condition is that the number of iterations g reaches the maximum number of iterations MaxGen or G b The number of times Nu whose fitness value remains unchanged reaches the specified number Kbest, if satisfied, continue to step 13, otherwise return to step 5; step 13, output individual G b and its fitness value f Gb ; Step 14, for the optimal individual G b and its fitness value f Gb to interpret. The invention aims to improve the search efficiency and convergence speed of the algorithm.

Description

technical field [0001] The invention relates to a multi-depot and multi-model vehicle path scheduling control method. Background technique [0002] Vehicle Routing Problem (VRP) is the core issue of logistics transportation management optimization. With the rapid development of science and technology and the steady growth of the national economy, people's daily life has become increasingly inseparable from the development of the logistics industry. Traditional The current logistics operation mode can no longer meet people's diversified and punctual requirements, and cannot keep up with the development of the times. The society urgently needs a high-efficiency and low-cost logistics operation mode. Therefore, it is necessary to study complex vehicle routes such as multiple depots and multiple models The question has strong practical significance. [0003] The vehicle routing problem refers to a certain number of customers, each with a certain number of goods demand, the depo...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing multi-depot and multi-model vehicle route scheduling control methods, such as poor versatility, low search efficiency, narrow search space, slow convergence speed, low stability, and poor solution quality, the present invention provides a highly versatile, Improved genetic extremum optimization algorithm with high search efficiency, wide search space, fast convergence speed, high quality and high stability

Images